User guide

NumPy User Guide, Release 1.9.0

and types

>>> x = np.array([[ 1.+0.j, 2.+0.j], [ 0.+0.j, 0.+0.j], [ 1.+1.j, 3.+0.j]])

2.2.3 Intrinsic Numpy Array Creation

Numpy has built-in functions for creating arrays from scratch:

zeros(shape) will create an array ﬁlled with 0 values with the speciﬁed shape. The default dtype is ﬂoat64.

>>> np.zeros((2, 3)) array([[ 0., 0., 0.], [ 0., 0., 0.]])

ones(shape) will create an array ﬁlled with 1 values. It is identical to zeros in all other respects.

arange() will create arrays with regularly incrementing values. Check the docstring for complete information on the

various ways it can be used. A few examples will be given here:

>>> np.arange(10)

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

>>> np.arange(2, 10, dtype=np.float)

array([ 2., 3., 4., 5., 6., 7., 8., 9.])

>>> np.arange(2, 3, 0.1)

array([ 2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9])

Note that there are some subtleties regarding the last usage that the user should be aware of that are described in the

arange docstring.

linspace() will create arrays with a speciﬁed number of elements, and spaced equally between the speciﬁed beginning

and end values. For example:

>>> np.linspace(1., 4., 6)

array([ 1. , 1.6, 2.2, 2.8, 3.4, 4. ])

The advantage of this creation function is that one can guarantee the number of elements and the starting and end

point, which arange() generally will not do for arbitrary start, stop, and step values.

indices() will create a set of arrays (stacked as a one-higher dimensioned array), one per dimension with each repre-

senting variation in that dimension. An example illustrates much better than a verbal description:

>>> np.indices((3,3))

array([[[0, 0, 0], [1, 1, 1], [2, 2, 2]], [[0, 1, 2], [0, 1, 2], [0, 1, 2]]])

This is particularly useful for evaluating functions of multiple dimensions on a regular grid.

2.2.4 Reading Arrays From Disk

This is presumably the most common case of large array creation. The details, of course, depend greatly on the format

of data on disk and so this section can only give general pointers on how to handle various formats.

Standard Binary Formats

Various ﬁelds have standard formats for array data. The following lists the ones with known python libraries to read

them and return numpy arrays (there may be others for which it is possible to read and convert to numpy arrays so

check the last section as well)

HDF5: PyTables

FITS: PyFITS

12 Chapter 2. Numpy basics